Matlab and Python toolbox for fast Total Variation proximity operators
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Updated
Feb 20, 2020 - C++
Matlab and Python toolbox for fast Total Variation proximity operators
Proximal operators for nonsmooth optimization in Julia
Proximal algorithms for nonsmooth optimization in Julia
A Python convex optimization package using proximal splitting methods
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
A Matlab convex optimization toolbox using proximal splitting methods
Implementation of "Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems"
A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.
A Julia package for manipulation of univariate piecewise quadratic functions.
Primal-Dual Solver for Inverse Problems
MATLAB implementations of a variety of machine learning/signal processing algorithms.
New Matrix Factorization Algorithms based on Bregman Proximal Gradient: BPG-MF, CoCaIn BPG-MF, BPG-MF-WB
An efficient GPU-compatible library built on PyTorch, offering a wide range of proximal operators and constraints for optimization and machine learning tasks.
Hybrid Approach to Sparse Group Fused Lasso
A Python package which implements the Elastic Net using the (accelerated) proximal gradient method.
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks
CoCaIn BPG escapes Spurious Stationary Points
Codes of `Tensor Robust Principal Component Analysis` expreiments.
A C/x86 assembly implementation of proximal operators with SSE3/AVX SIMD instructions
Fortran code implementing Newton-like algorithms for proximal mapping of total variation.
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